{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64751d55-2290-43b7-bb35-47e5d7dc81c5",
   "metadata": {},
   "source": [
    "* Matplotlib是基于python的第三方2D制图库\n",
    "* 可以生成折线图、柱状图、直方图、饼状图、散点图和箱线图等\n",
    "* pandas，networkx等数据分析库的可视化部分都是调用Matplotlib\n",
    "* seaborn也是在在Matplotlib的基础上开发的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5275a83-7e84-4e7f-a36d-7ea8c98790a1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 一、安装\n",
    "\n",
    "### 1.1 conda安装\n",
    "\n",
    "```bash\n",
    "conda install matplotlib\n",
    "```\n",
    "\n",
    "### 1.2 pip安装\n",
    "\n",
    "```bash\n",
    "pip install matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0e0d40-1faf-4802-aad2-83f5b6cab86a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 二、简单使用（以折线图为例）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d831fd3e-a16b-4be4-bae7-99e2c7b7297d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:30.086158Z",
     "iopub.status.busy": "2022-06-11T04:02:30.085877Z",
     "iopub.status.idle": "2022-06-11T04:02:30.648598Z",
     "shell.execute_reply": "2022-06-11T04:02:30.647948Z",
     "shell.execute_reply.started": "2022-06-11T04:02:30.086096Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import matplotlib.pyplot as plt  # 导入画笔\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b79cde9-f61f-47c1-bd80-41e47a0f8a0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:32.733462Z",
     "iopub.status.busy": "2022-06-11T04:02:32.732906Z",
     "iopub.status.idle": "2022-06-11T04:02:32.744638Z",
     "shell.execute_reply": "2022-06-11T04:02:32.743935Z",
     "shell.execute_reply.started": "2022-06-11T04:02:32.733436Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 6, 1, 7, 6, 6, 10, 10, 3, 6]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个list对象\n",
    "# list中有10个元素\n",
    "# 每个元素都是0-12之间随机整数\n",
    "yData = [np.random.randint(0, 12) for x in range(10)]\n",
    "yData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b5df22-4798-425d-88e2-004fd825dd0e",
   "metadata": {},
   "source": [
    "### plt.plot()函数\n",
    "```\n",
    "plot([x], y, [fmt], data=None, **kwargs)\n",
    "```\n",
    "\n",
    "1. plt.plot()函数可以只传y轴数据\n",
    "    1. 只传y轴数据，matplotlib会默认调用range(0,len(y轴数据))生成x轴数据\n",
    "2. plt.plot()函数的x和y轴参数只能按位置传参\n",
    "3. 可以读取list、dict或者DataFrame形式的数据\n",
    "4. 可以设置线样式和颜色"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93137843-2e90-43ea-b236-2a2963879f20",
   "metadata": {},
   "source": [
    "### 2.1 列表传参绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d94d1225-9e40-4e0b-aee2-09e32ee2479c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:39.271085Z",
     "iopub.status.busy": "2022-06-11T04:02:39.270625Z",
     "iopub.status.idle": "2022-06-11T04:02:39.400366Z",
     "shell.execute_reply": "2022-06-11T04:02:39.399457Z",
     "shell.execute_reply.started": "2022-06-11T04:02:39.271058Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10be0aa10>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "# 数据(yData)默认传给Y轴取值\n",
    "# 如果不手动指定X轴数据，matplotlib会调用range函数自动产生x轴数据（个数跟yData对应）\n",
    "plt.plot(yData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99401891-23ae-48bf-bc35-5a103dd8192d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:42.159332Z",
     "iopub.status.busy": "2022-06-11T04:02:42.159020Z",
     "iopub.status.idle": "2022-06-11T04:02:42.271346Z",
     "shell.execute_reply": "2022-06-11T04:02:42.270279Z",
     "shell.execute_reply.started": "2022-06-11T04:02:42.159309Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10beffd90>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "# 数据(yData)默认传给Y轴取值\n",
    "# 手动指定X轴数据\n",
    "xData=np.arange(15, 25)\n",
    "plt.plot(xData, yData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92543a56-235d-4625-b71e-f9b13e846487",
   "metadata": {},
   "source": [
    "### 2.2 字典传参绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "015c4047-e9d7-4a79-bb2d-1b9147cbad27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:45.294711Z",
     "iopub.status.busy": "2022-06-11T04:02:45.294376Z",
     "iopub.status.idle": "2022-06-11T04:02:45.301968Z",
     "shell.execute_reply": "2022-06-11T04:02:45.301200Z",
     "shell.execute_reply.started": "2022-06-11T04:02:45.294687Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': array([ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17]),\n",
       " 'key2': [4, 5, 9, 4, 11, 7, 8, 11, 10, 4, 9, 11]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建字典\n",
    "data = {\n",
    "    \"key1\": np.arange(6,18),\n",
    "    \"key2\": [np.random.randint(4, 12) for x in range(12)]\n",
    "}\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2a98e3a-9d56-4789-9f7e-e74ff22369cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:48.005175Z",
     "iopub.status.busy": "2022-06-11T04:02:48.004859Z",
     "iopub.status.idle": "2022-06-11T04:02:48.127125Z",
     "shell.execute_reply": "2022-06-11T04:02:48.126339Z",
     "shell.execute_reply.started": "2022-06-11T04:02:48.005152Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10bf72f20>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "# key1做x轴，key2做y轴\n",
    "plt.plot(\"key1\",\"key2\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e981a47f-e04c-4af5-8d14-2517e27d7e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:51.072495Z",
     "iopub.status.busy": "2022-06-11T04:02:51.072033Z",
     "iopub.status.idle": "2022-06-11T04:02:51.192372Z",
     "shell.execute_reply": "2022-06-11T04:02:51.191488Z",
     "shell.execute_reply.started": "2022-06-11T04:02:51.072426Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10bfe2a40>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "# key2做x轴，key1做y轴\n",
    "plt.plot(\"key2\",\"key1\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25c7cc3c-3f1a-46b7-a578-90d042ed9f9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:53.812496Z",
     "iopub.status.busy": "2022-06-11T04:02:53.812171Z",
     "iopub.status.idle": "2022-06-11T04:02:53.955588Z",
     "shell.execute_reply": "2022-06-11T04:02:53.954536Z",
     "shell.execute_reply.started": "2022-06-11T04:02:53.812471Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c04e230>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "# 只传y轴数据（以key1为例）\n",
    "plt.plot(\"key1\",data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b33501-25e8-4b93-ab95-b419d96e49a6",
   "metadata": {},
   "source": [
    "### 2.3 DataFrame传参绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4229b7f-c879-4fd0-aa60-2b9506adfb84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:56.591141Z",
     "iopub.status.busy": "2022-06-11T04:02:56.590808Z",
     "iopub.status.idle": "2022-06-11T04:02:56.798985Z",
     "shell.execute_reply": "2022-06-11T04:02:56.798378Z",
     "shell.execute_reply.started": "2022-06-11T04:02:56.591117Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    key1  key2\n",
       "0      6     5\n",
       "1      7     6\n",
       "2      8     4\n",
       "3      9     7\n",
       "4     10     4\n",
       "5     11     6\n",
       "6     12     7\n",
       "7     13     9\n",
       "8     14     9\n",
       "9     15     5\n",
       "10    16    11\n",
       "11    17     8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "myData = pd.DataFrame(\n",
    "    {\n",
    "        \"key1\": np.arange(6,18),\n",
    "        \"key2\": [np.random.randint(4, 12) for x in range(12)]\n",
    "    }\n",
    ")\n",
    "myData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfebb76f-2a27-4862-b2b3-86bf0c8522c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:02:59.903562Z",
     "iopub.status.busy": "2022-06-11T04:02:59.903116Z",
     "iopub.status.idle": "2022-06-11T04:03:00.024910Z",
     "shell.execute_reply": "2022-06-11T04:03:00.023885Z",
     "shell.execute_reply.started": "2022-06-11T04:02:59.903535Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dcbb130>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "# 设定以key1列为x轴，以key2列为y轴\n",
    "plt.plot(\"key1\",\"key2\",data=myData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7cec427-3b5c-4e1a-b87e-dc0c4d006c59",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.4 线样式设置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8165d6-d4b8-47cd-83bf-1c2f29ef2324",
   "metadata": {
    "tags": []
   },
   "source": [
    "<img src=\"images/线样式.jpg\" alt=\"线样式\" style=\"zoom: 45%;\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7d66c36-e1f6-4b4e-8624-3e8bdd941745",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:03:04.612278Z",
     "iopub.status.busy": "2022-06-11T04:03:04.611966Z",
     "iopub.status.idle": "2022-06-11T04:03:04.752251Z",
     "shell.execute_reply": "2022-06-11T04:03:04.751547Z",
     "shell.execute_reply.started": "2022-06-11T04:03:04.612256Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dd32890>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制虚线\n",
    "plt.plot(\"key1\",\"key2\",\"--\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cdf11720-c6ef-4c65-891c-e6108f92465e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:03:07.897525Z",
     "iopub.status.busy": "2022-06-11T04:03:07.897197Z",
     "iopub.status.idle": "2022-06-11T04:03:08.016033Z",
     "shell.execute_reply": "2022-06-11T04:03:08.015018Z",
     "shell.execute_reply.started": "2022-06-11T04:03:07.897501Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dd9e290>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制折点：大点\n",
    "plt.plot(\"key1\",\"key2\",\"o\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b71d5826-aa33-485b-b8dd-3cce5e946459",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:03:10.710972Z",
     "iopub.status.busy": "2022-06-11T04:03:10.710649Z",
     "iopub.status.idle": "2022-06-11T04:03:10.828118Z",
     "shell.execute_reply": "2022-06-11T04:03:10.827150Z",
     "shell.execute_reply.started": "2022-06-11T04:03:10.710949Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10de09ed0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制折点：小点\n",
    "plt.plot(\"key1\",\"key2\",\".\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e46301cb-c4b6-43f1-b968-a2b3c8c9fa3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:04:08.066214Z",
     "iopub.status.busy": "2022-06-11T04:04:08.065922Z",
     "iopub.status.idle": "2022-06-11T04:04:08.272102Z",
     "shell.execute_reply": "2022-06-11T04:04:08.270930Z",
     "shell.execute_reply.started": "2022-06-11T04:04:08.066196Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17b292470>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NHPrif66dTMKgyIC3Y93fFiEGiEfXFRMX7eD2C8aaHco5Halr442tRyw3Dl7s7NpGLTvVGj1wgJS46KC8GUsCF8JE+yqbWL2rkq9fMJb4QcHfFLc/5h6vB7fY8rJXTE7j3e9cSMawWLNDOcVb245w61MbA1q5IwlcCBNtL6tnaEwEd1qw8qSnccmDSYqNJP+gterBoyPsTB4eT4TdWunM072b0vbyhoC1Ya0zFmKAufn8UXz6o4UkxAR+vNRfSilmZySSH4TyuP54fH2xJWrAe7psYioOm2JVYUXA2pAELoRJSmtagK6qhVAxP2sYyXFRNLd3mh0KAB2dXv6wZn9QKj76Kz4mgvlZw1i1szJgwyiSwIUwQcmxFi79/Xpe2lBidij98pW5Y3jr/vkMjrLGm05JTQser7bUDcyTLclJ43BtK7uONgbk+H4lcKXUd5VSu5RShUqpV5RS1l/1XQgLeHx9MRF2G1dMNnfxJV9ZZWnZoqquChSr1ID3tHhyGv8yJZ36Vjc3/3UDziZjdwvyOYErpUYA3wFytdY5gB34slGBCRGuympbeWPrEW6ZPZqUIaHX5/n9e/tYuuxjs8MAoMjZhFJdN1itKDE2ksdum8nqwgo2l9SyLK/Y0OP7O4TiAAYppRxADGCNFeiFsLAnPjyATSnuvSTT7FB8khATwd7KJqoag7/3ZE9H69sYNTTGcot/nczZ6OK1LeVoDSsKygzthfucwLXWR4DfA4eBCqBBa72m5+uUUvcopQqUUgXV1cHdsVkIq3G5Pby/u4qbckeSHh/8vRuNMDvjeD24+dUov71xGqsevMjsMM7pT2v34+2+ienR2tBeuD9DKEOBa4AMYDgQq5T6Ss/Xaa2f1Frnaq1zk5OTfY9UiDAQHWFn3fcu4XuLx5sdis8mpQ9hcJTDMhs8xFrkhuqZOBtdvLH1CG5PVwJ3e7ShvXB/hlAuAw5prau11m7gDeACQ6ISIgy1dXjwejVx0REkxlq/7vtsHHYbuWOHmt4DL6tt5YFXtrE7QBUeRliWV3Si932ckb1wfxL4YWCuUipGda1sswjYY0hUQoShP6zpuvnX3ukxOxS/3TRrFNfNGGFqNcruikb+uf0obo81V3AE2Hq4/kTv+zi3R7O1tM6Q4/t87aG1zldKrQC2Ap3ANuBJQ6ISIswca27n5fxSluakW2avS3/8y9R0s0M4sYjVOIuWEAKsDPD4vF+DR1rrnwI/NSgWIcLW0x8for3Ty/0Ls8wOxTANbW6qGl2cF8Rd2E9W7GxmeHy0ZSYVmUFmYgoRYHUtHby0oYQvTR1u2XplX9z/t61855VtprVf5Gwiy6Q3D6uQBC5EgL1aUEar28O3w6j3DV3lhPuqmqhv7TCl/WiHnZzhQ0xp2yoG7rWHEEFy14UZTBuVYNpQQ6DMyUhEa9hcUsflk1KD3v6Kb0nRm/TAhQggrTUOu425mYHd3NYM00YlEOmwWaYefCCSBC5EgDS53Fz2xw95b1el2aEERHSEnemjEthUEvx68Jc3lnLTXz4Li5JMf8gQihAB8uKGUg5Ut5AeH3oLVvXVj5dOJCYy+GWR28vqKalpDYuSTH9IAhciAFraO3n644MsGJ/M1JEJZocTMNNHJZjSbpGzmWwL138HiwyhCBEAf8svpa7VzQMLs80OJeD+uf0oqwuDN0yktaZYEjggPXAhDOdye3jyo0NcmDWMWWOGmh1OwD336SEArswJzuYUlY0umts7B3wNOEgCF8JwUQ4by26ZzpDoCLNDCYrZGUk8/fFBWjs6g7K/p8vtZdGEFKaMiA94W1YnQyhCGEwpxQXjhpEzQBLMnMxEOr2abYfrg9JexrBYnrnjfNPG361EErgQBnp1cxk/e2fXgCpvyx0zFJsiaPXgHovsx2kFMoQihEE6Or08vHY/afHRRNoHTt8oLjqCycPjOXCsJSjt3fLURpIHR/HYbTOD0p6VSQIXwiBvbC3naIOL/71+Cl1L5A8cr9wzNyirAmqt2VfZxLgpUoECMoQihCHcHi+PrS9m6sh4Ljlv4G0dGKwlXY81d9DQ5pYSwm6SwIUwwNufH6Wsto0HFmYPuN43gNer+fbyrTzfXVIYKMc3cchOlQQOksCFMMSM0Qnct2Acl01MMTsUU9hsigPVLby/pyqg7RQ7mwDITpEacJAELoQhxiUP5t+vnDAge9/HzclIZEtpHR2dgdujMisljjsuGEvqkKiAtRFKJIEL4QevV/Pf/9zFvsoms0Mx3dzMRFxuLzuPNASsjXnjkvjZ1ZMH9BvlyXxO4Eqp8Uqpz0/616iU+jcDYxMB4Gx0cfNfN+BscpkdSshzNrpY/PBHPPdpCfurJIGfPzYRgPxDgasHL61pkTrwk/icwLXW+7TW07XW04FZQCvwplGBicBYllfE5pJaluUVmx1KyHskr4hiZzNx0Q6WTjF/l3azJQ2OYumUNBIGRQbk+PWtHVzyu/U888nBgBw/FBk1hLIIOKC1LjXoeCIAnI0uXi0oR2tYUVAmvXA/dH0vy4CuxatqWtpNjsgaHr9tFrfOGR2QY5+oQJEbmCcYlcC/DLxypieUUvcopQqUUgXV1dUGNSd8sSyviA5P1w0mj9bSC/fDz9/djdvzxaW8fC+/0Onx0tZh/FICRd0JPEtqwE/wO4ErpSKBq4HXzvS81vpJrXWu1jo3OXngTXCwCmeji9e2lJ/43O3R0gv3kbPRxZpdX5TLyffyCw1tbqb99xpe3mj8xXhRVTODIuyMSBhk+LFDlRE98CXAVq11YAtAhV+W5RXh1afe/JFeeP/tOtrAr1btke/lWcQPiiBlSHRAbmQWOZsYlxKLzSYVKMcZMf/1Fs4yfCKsY+vh+lMu+aGr57i1tM6kiELPnopGbns6n45Or3wvz2H22ERWFVbg9WpDk+03LsrE5R44qzz2hV8JXCkVA1wO3GtMOCJQ/nHvXGb9fC1fmTuGq6cP50ev7+BP/zqdielDzA4tJOyvauK2p/MZFGHnnfsvZHRSjNkhWdaczET+UVDG3somJg037vdrIK4x0xu/hlC01q1a6yStdeAq94Uh1u110uHxsnRKGtNHJbD63y6W5N1Hxc5mbn0qH4dNsfzuuZK8ezEnMwkwth7c2eRiw4Ea6YH3IDMxB4hVOytJiYti5ugv9mh0uT0DauMBX/3y3d0ALL97LhnDYk2OxvpGJAzioSUTmJORZNgxP9xXzS1PbaSiQW4Un0wS+ADQ2tHJ+v1OrsxJOzEmebC6mZk/fz+ou4mHqj/963T+fs9cKV/rh3svGWfo8Emxs5lIu41RQ6UC5WSSwAeATq/mgYXZ3DBz5InHxibFMjjKwaqdksDPpKy2lYfe2InL7SEhJlKSdz+53B4+2l9NVaMxPeZiZzOZybE4BtBOR30h340BYEh0BPdfmsW0kzaBtdkUV+aksX6/k9aOTvOCs6Cj9W3c+vRG3t1xlPK6NrPDCUmVDS6+9uwm1uw2prq4yNksb6JnIAk8zLncHt7dUXHGJL0kJx2X28v6fTJD9rjKBhe3PLWR+lY3L39jjiQNH41JiiF1SBSbDtX6fay2Dg9lda0yhf4MJIGHuY+LjnH/8q0UlJxeozw7I5Gk2EhWyTg40DXD8tanNlLT3MGLd85m6sgEs0MKWUop5mQkkX+wBq39Wz0wwq548775XD9zhEHRhQ/Z1DjMrdpZQfygCOaNO70iwG5T/PK6KYyUG0NA136L7Z1env/6+cw4qVpH+GZOZiLvbD9KSU2rX9U7DruN6ScN/4kvSAIPYx2dXt7fU8UVk9OIOMvNnytz0oIclfW0dXgYFGln0vAhfPD9BUQ65MLUCHMyutYH33Soxq8E/nFRNfWtbr40bbhRoYUN+U0NY58eOEaTq5MlvSTpjQdreHVzWZCispa6lg6ue/xTHl1XBCDJ20Djkgfzfw9cyI2zRvl1nJc3lvLw2v0GRRVe5Lc1jG08UMPgKAcXZg875+te31LOz9/dPeAm9TS0uvnqs/kcPNYi490BoJQiZ0Q8dj/XQylyNssNzLOQBB7GfrRkAmu+ezFRDvs5X7dkShpNrk4+Kw7cVlhW0+hy87Vn89lf2cxfvzKLi2WdjYA4dKyFn7xdyNF638ox2zs9lNa0kp0q1UBnIgk8jCmlGN6HtZPnZw0jLsrBqsKKIERlPo9Xc+dzm9l1tJHHb5vJpRNSzA4pbLncHl7cUMqGA751DkqOteLxainnPAtJ4GHqN6v38tO3C/v02iiHnUUTU1izuwp394494cxuU9wyezSP3jqDyyalmh1OWBufGkf8oAifF7Y6UC278JyLJPAw5PFqXiso41hLR5+/ZsmUdBw2G4drWwMYmbnaOjxs6V6z+4ZZI7kyRzYiDjSbTXH+2ESfJ/QsyUlj40OLOC9VxsDPRBJ4GNpcUsux5o5eq09OtmhCCvk/XsS45PDs6bjcHu5+sYDbnt4oW58F2dzMREpqWn1aF0UpRVp89FnLYAc6+a6EodWFlUQ5bFw6vu9juw67DbtN4fVqvF7/Zs5ZTXunh3tf2sKnB47xi2unkBIXbXZIA8qcjCRSh0RRXtf/q7vfrt7Lqp0D496MLySBhxmvV7OqsIJLzksmNqp/87R2H21k/m/WkW/A+hVW0dHp5b6Xt/Lh/mp+dd0Ubpw1svcvEobKGTGEjQ8tYtaYxH59XafHy1MfH+Tz8vrABBYGJIGHmTa3hyU56T4lqjFJMdS1drA6jKpRXttSRt5eJz+/Nocvzx5tdjgDklIKpfpfC15a24rbo6UG/BwkgYeZ2CgHP7t6Mosn93+KfGyUg0vOS2ZVYWXYDKPccv5olt89h6/OHWN2KAPa2t1VzP/1Oo41t/f5a4qdXRUo2VKBclZ+JXClVIJSaoVSaq9Sao9Sap5RgZnJ2eji5r9uCLmbXVprtpTW+pV8l05Jx9nUzray0Nxh3dno4ua/fMaP39hBWW0rNpvignHnnokqAm9obCRH6tvY3I/hueMJfJwk8LPytwf+CLBaaz0BmAbs8T8k8y3LK2JzSS3L8orNDqVfdpQ3cMMTG3h7+xGfj7FwQgqRdhsrQ3SnnkfyithUUsfyTWV8sM9pdjii29SR8QyKsPfr/kpjm5uxSTEM7ue9nIHE5wSulBoCXAw8A6C17tBa1xsUl2mcjS5e21KO1rCioCykeuGrCitx2FS/qk96iouO4EdLJrBoYujNTqysb+OVTYcBcHTvOCSsIcJuY9aYoWw82PcJPQ8tnci67y0IXFBhwJ8eeCZQDTynlNqmlHpaKXXampFKqXuUUgVKqYLqauvv/LIsrwhP9wL0Hq1DpheudVf1ybxxSSTERPp1rDsvzAjJYYfvrdjO8dEjpQiZn91AMScjkX1VTdS39n2Cmc3PhbDCnT8J3AHMBJ7QWs8AWoAf9XyR1vpJrXWu1jo3OdnaCwat2VXJa1vK6fR0ZQG3R4dML3xPRROlNa0snWLM7MI9FY18tN/6b7jHORtdp6y3EUo/u4HikvHJ3Dp7NC5378s1lNe1cutTG9lSGj4lrYHgTwIvB8q11vndn6+gK6GHpI/2V/PNl7ecthZIqPTC1+yuxKZgsUFre/xq1V7+861Cv7fDCpZleUWnLVsaKj+7gWLqyAR+ed0U0uJ7n0i1v6qJz3xcAGsg8TmBa60rgTKl1PjuhxYBuw2JKsg+Kz7G3S8WEGG30bOAw+3RbC21fkXGfQuyeP1bF5A0OMqQ4y3NSeNwbSu7KxoNOV4geb2agtI63J5Tf3ih8rMbSLxefWKBqnMpqupexCpZasDPxd/buw8Af1NKRQIHga/7H1Jw5R+s4a4XChiTFMMrd889kQAbWt3M/806LhmfzGO3Wv/CItJhM3Qfx8snpfLjN3eyamclk4fHG3bcQHh/TxWVjS7e+7eLGZ8mf/BW9ugHxTy8dj+f/3QxQ6Ijzvq6ImczyXFRxMec/TXCzzJCrfXn3ePbU7XW12qtQ6q70+nx8tAbOxmeEM3fvjH3lN5rfEwE916cyYiEQZYfRlief5jfvbfX0DiTBkcxNzOJlYUVlj5/rTV/XldE/KAIxiX7vu+iCI7cMUPxathScu5U0bULj9R/92ZAF1g67DaeveN8YiLtJMedPvTwwKJsE6Lqvxc3lBAX7fBpuvK5LJmSzi/+bzeVjS7S4625c/0H+5wUHmnktzdOxSEr1lnejNFDibArNh6qOedGGsPjo2UJ2T4YkAl8R3k97+2q5PuLxzO2l92ytdZ8WlzD6MQYRifFBCnCvjtY3czeyiZ+ctUkw499w8wRXD9jRL8XxQoW3X2TcuTQQVw3Y4TZ4Yg+GBRpZ+rIhF7XB3/iK7OCFFFoG3BdlsIjDXz1mU28te0oda3uXl9f1+rmGy9uZln3ruVWs6qwa8ZkICatxEQ6LJu8AT4vq+fzsnruW5Al60WHkDkZiewsb6C1o9PsUELegPqt31vZyFefySc20s7f75lLYmzvE14SYyO5dfYY3tx2hDIL7lazurCS6aMS+rT3pS92lNfzpT9/0qfKgWCbMXoor39rHjfMkt53KLlh1kieuj0Xh+3M6eeFz0pY+Pv1NLdLgu/NgEngRVVN3PZUPpEOG8vvnsuoxL4Ph9x7SSZ2m+Lx9daqKe7o9DI6KYbrZwYugSXHRbHzSAOrC621NsrxBbtmjUkkymE3ORrRH+OSB3Pp+BQiHWdOP3srG6lr7ZA1UPpgwCTww7WtREfYeeXuub2Oe/eUOiSaf80dxYot5RypbwtQhP0X6bDx2K0z+dq8sQFrIz1+EDNGJ1hux/qvP7+ZP67ZZ3YYwkeFRxp4dXPZGZ8rdjbLGuB9FPYJ3OX2ALBoYip537uETB/3fPzmgnGkxEVTcqzFyPD8cjRIbyZLc9IpPNLI4RprDCFtLqnlw/3VDBkkNcKh6p3tR/nPtwpP/H0ep7Vmf1UzWalSQtgXYZ3Ay2pbueyPH/Lujq7eY3SE75faIxIG8dG/X8r8LGss8lTR0MYFv17H3/JLA97W8Rukq3dZoxe+LK+IYYMjuW2ObNIQqmaPTaTD4+XzsvpTHj/W3EFDm1tqwPsobBN4eV0rX35yI83tnWT0c8jkbOw2hfsMv3RmOD4mPS8zKeBtjUqM4Y4Lxlpix/pth+v4uOgY37gok0GRMvYdqs7PSEQpyD94ajmh2+PlplkjDZ1VHM7C8i5BRUMbtz6VT5PLzfK75zJp+BDDjv2bVXt5aWMpH//wUlN3N1+1s5IJaXE+Dwn118+unhyUdnrz2AfFDI2JkC3SQlz8oAgmpg0h/1AN8MWEueEJg/jdTdPMCyzEhF0PvNHl5tan8qlr6eClu+aQM8LYdTxumzsGt8fL0x8fMvS4/eFscrG5tDboGxZUNbrYY/LiVv911ST+cPM0S9eni76ZnZHI3somPCetINfoclt66QarCbsEHhfl4NrpI3j+ztlMG5Vg+PEzhsVy9bThvLShlJp+bNBqpPd2VaE1hq393Vd3PLeZn769K6ht9jQmKZaFE4xZMleY67uXncfGhxadsgzwvS9u4ZanNpoYVWgJmwRe09xOsbMJpRQPXpbNrDGBG0P79sIsXJ0envnEnF741VOH89itM4N+o+fKyWlsLq3F2Rj8TRL2VjbyjRc2W3IylfBNfEzEabXgRc5mRvdjjsZAFxYJvK6lg9uezuf2ZzfT0dn7bh/+ykqJY+mUdPIP1ZpyuRcfE8G/TE03fPGq3iydkobW8N6u4E/q+fO6YjYerCUuWoZOwsnTHx/kv//ZdVVX19LBseZ2qQHvh5BP4PWtXcn70LEWfnPD1LPO7jLar66fwmv3zgt6El23t4pnPzl02s5BwZCdGse45NgT668ES7GziZU7K/javDF+7/cprKWkpoVXN5fR6fFS3L1cQ5aUEPZZSCfwhjY3X31mE8XOZp78Wi4XZgevRntIdAQ2m6LR5aYliGs2PP9ZKS9sKMFh0mavS6eks+lQLQ1tvS8EZpRH1xUT7bBz14UZQWtTBMecjCRaOjzsOtpIsVMSeH+FdAJfllfE3spGnvjKTC45L/gbJtc0t3Phr9fx3KfBGQtvaHXzWfExrsxJC3rP/7ivzRvLJz9cSHyQZkEeOtbCO9uP8tV5YwzbLk5Yx5yMRADyD9UwZUQ8Dy7KZkSAFmYLRyE9oPiDK8azeFIqc4IwmeVMkgZHkTs2kac/OcQd8zMCvvjO+3uq6PRqluYEt/rkZGfa+CKQEmMieWBhNrfNHR3UdkVwpAyJJmNYLJsO1XLPxeMML/sNdyHXA2/t6ORn7+yi0eUmOsJuWvI+7oGFWdS3unl5Y+CntK/aWcGIhEFMHWnuL/n2snruen4zDX1YT91f8TERfPfy80ydNCUCa/HkVBJjI9leVk+TK3hDc+EgpBJ4W4eHb7xQwIsbSnrdUy9YZoweykXZw3j644O0dXh6/wIfaa3p8HhZOsW84ZOT5e118v6eqoC28ZcPD7B2d2DbEOZ7aMlE/uuqSVzz2Ke8uCHwHaFw4lcCV0qVKKV2KqU+V0oVGBVUT85GFzc+8Rl3PLeJDQdr+MPN0865n16wfWdRNseaO/hgnzNgbSileOmuOfx46cSAtdFXU0fGMyJhEKt2Bm5xqyP1bfxhzT4+3F8dsDaEdWzu7pClBHmILtQZ0QO/VGs9XWuda8CxzuhPa/dTUFpH/qFafnPDVK6bMTJQTfnk/LGJrPnuxQGdGXl82U0r9L6VUlyZk8bHRccCdsn7l/UHgK5lfEX4u/P5zQB8UnTM5EhCi+WHUJyNLl7fegQAh02xYHzwq0364vgO2u2dxg+jtLR3cv4v1vLihhLDj+2rJTlpdHi8rNtr/FVHZYOLf2wu48ZZI6UiYQA4eWbve7sqcTYFf6ZvqPI3gWtgjVJqi1LqnjO9QCl1j1KqQClVUF3d/8vhZXlFJ2Y7KgXL8qy1rdnJnv74IJf/8SPDk/j6fdU0tXeeeJOwgpndY/+RAdhM+K8fHcCjNd+6JMvwYwvrWZZXRIS968rSo7Wl/8atxt+/vvla65nAEuB+pdTFPV+gtX5Sa52rtc5NTu5f79nZ6OK1LeW4PV0J3O3RrCgos+w79HmpcRyubWXFlnJDj7uysIJhgyM5f2yiocf1h83WNSa/JADDRpPSh/DNSzIZnSRrYoS7UPsbtxq/ErjW+mj3/07gTWC2EUEdtyyvCG+PtUas/A59UfYwpo9K4In1Bwyb6u5ye/hgr5PFk9NOWbXNKto6PFQ2GPvHdlPuKH5wxQRDjymsKdT+xq3G5wSulIpVSsUd/xhYDBQaFRjA1sP1J96Zj3N7NFtLrVFC2JNSiu8syqK8ro03tx0x5Jgf7q+mtcPDkiCv/d0XWmsWP/whP393tyHHq23p4IXPSk7bJ1GEr1D7G7caf6YOpgJvdldFOIDlWuvVhkTVbeWDFxl5uKC4dHwKOSOG8NcPD3DTrJF+V41MHRnPj5dOYK7JE5bORCnFRdnJvLXtCC63x689RwGe+eQgj68/wAXjksi20Hi/CJxQ/Bu3Ep8TuNb6ICB7H/WglOLX108lflCEISV/6fGDuOdi65bSLclJY3n+YT7cX80Vk32/Sqhv7eCFz0pZmpMuyVuIPrJ8GWEoyhkRz6juRen9WS98Z3kD/9x+NChrnPtqbmYSCTERJzZZ9tVzn5bQ3N7JtxdK5YkQfSUJPEBqWzq4/dlN/N8O32crvrihhB+/sRONdfcIjLDbWDwplbW7q3wun2x0uXnu00MsnpTKxHTjNqAWItxJAg+QhEERHKlv49F1xXi9/U/Abo+X9/dUcdmkVKIc/o0tB9o9F4/jlXvm+lwTfqypnXEpg3lgYXbvLxZCnCAJPEBsNsW3L81iX1UTa3b3f3hh48Ea6lvdlqw+6SkrZTA5I+J9HvPPTB7Mm/fNZ4rJqywKEWokgQfQVVPTyRgWy7K84n6Pha/cWUlspJ2LTdiowhe7jjbwn2/t7Hf9e/7BGmqa2wMUlRDhTRJ4ADnsNu5bMI7dFY3k7enfmiHFziYunZDid2lesBypa+PljYfZcKCmz1/T1uHh/uVb+eHrOwMYmRDhK6R35AkF184YQUObm/Mz+jcN/tV759EWQhNaLj4vmdhIO6sKK/t81bB802GONXfwzUsyAxydEOFJeuABFmG38Y2LMvu1h6TWGqUUMZGh8/4aHWHn0gkprNlViacPN21dbg9/+fAA8zKTyLXQGi9ChBJJ4EHywT4nP1yxo9excK9Xc8XDH/HCZyXBCcxAS6ekU9PSwaZDtb2+9h+by6huaueBRVL3LYSvJIEHSXltK/8oKOt1jHhbWR37q5pJiAnOru9GWjA+mbFJMdS09H5T8mB1M7PHJjLPgksECBEqQucaPcTdlDuKRz8oZtm6Ii7IGnbW163cWUmk3cZCC20Z11cxkQ4++P6CPpUT/vc1ObR3eiyxw5AQoUp64EESHWHn3ovHsfFg7VmHGLTWrC6s5KLsYcRFh14PHLrWgvF6NS3tnWd83u3xcqC6GcDyE5SEsDpJ4EF0y+zRDBscxZ/XFZ3x+R3lDRypbwvIJgnB0unxsuD36/nDmv1nfP7NbUe47I8fUnikIciRCRF+JIEH0aBIOz9aMoEvTRt+xpuZCTER3Dk/g8snppoQnTEcdhvnpQ5mdWHFaefY6fHy+AfFTB4+hMnDZc0TIfwlCTzIbpw1kptzR51x7HdMUiw/+dIk4kPwBubJluSkc7TBxfbyU3vZ/9xxlJKaVh5YmC1j30IYQBK4CVxuD898cuiUYYSy2lY2HqzpUw211V02MRWHTbFq5xcrMXq8mkfXFTMhLS6krzCEsBJJ4CZwe7w8snY/j+R9MRb+j81l3PrURupbO0yMzBjxMRHMzxrGqsLKE8MoeyoaKa9r44GF2dgsuLenEKFIyghNEBcdwZ0XZvDw2iJ2H21kYnocKwsrmJuZRNLgKLPDM8R9C8bR6vagNSjVtcnFJz9cSFJspNmhCRE2pAdukq9fkEFclINHPyiiyNnMweqWkK4+6WlOZhKXjk/BZlM0tLrRWpMcFyW9byEM5HcPXCllBwqAI1rrq/wPaWCIj4ng9gvG8tj6Ytq7t0ybNSbB3KAMdqC6mbe2HuGZTw6xYEIyj982y+yQhAgrRvTAHwT2GHCcAeeuCzO4MGsYH+7rWmp2eX6ZyREZa8OBGv78QTGtbg91LW6zwxEi7PiVwJVSI4F/AZ42JpyBZWhsJH+4aRp2W9ePYUVBGc4ml8lRGWfm6IQTH287XBdW5yaEFfjbA38Y+HfgrNuwKKXuUUoVKKUKqqur/Wwu/CzLK8LbXanh0ZplecUmR2Sc5fmHsXcPeYfbuQlhBT4ncKXUVYBTa73lXK/TWj+ptc7VWucmJ4fG9mDB4mx08dqWctyergTu9uiw6YUfP7fuUwurcxPCKvzpgc8HrlZKlQB/BxYqpV42JKoB4uTe93Hh0lMN53MTwip8TuBa64e01iO11mOBLwPrtNZfMSyyAWDr4foTve/j3B7N1tI6kyIyTjifmxBWIRN5TLTywYvMDiFgwvnchLAKQxK41no9sN6IYwkhhOgbmYkphBAhShK4EEKEKEngQggRoiSBCyFEiFJn2torYI0pVQ2U+vjlw4BjBoZjNeF8fnJuoSuczy+Uzm2M1vq0mZBBTeD+UEoVaK1zzY4jUML5/OTcQlc4n184nJsMoQghRIiSBC6EECEqlBL4k2YHEGDhfH5ybqErnM8v5M8tZMbAhRBCnCqUeuBCCCFOIglcCCFCVEgkcKVUglJqhVJqr1Jqj1JqntkxGUUp9V2l1C6lVKFS6hWlVLTZMflDKfWsUsqplCo86bFEpdT7Sqmi7v+Hmhmjr85ybr/r/r3coZR6UymVYGKIPjvTuZ303PeVUlopNcyM2IxwtvNTSj2glNrX/Tf4W7Pi81VIJHDgEWC11noCMI0w2URZKTUC+A6Qq7XOAex0ra0eyp4Hruzx2I+APK11NpDX/Xkoep7Tz+19IEdrPRXYDzwU7KAM8jynnxtKqVHA5cDhYAdksOfpcX5KqUuBa4CpWuvJwO9NiMsvlk/gSqkhwMXAMwBa6w6tdb2pQRnLAQxSSjmAGOCoyfH4RWv9EVDb4+FrgBe6P34BuDaYMRnlTOemtV6jte7s/nQjMDLogRngLD83gD/Rte9tSFc7nOX8vgX8Wmvd3v0aZ9AD85PlEziQCVQDzymltimlnlZKxZodlBG01kfoetc/DFQADVrrNeZGFRCpWusKgO7/U0yOJ1DuBFaZHYRRlFJXA0e01tvNjiVAzgMuUkrlK6U+VEqdb3ZA/RUKCdwBzASe0FrPAFoI3UvwU3SPBV8DZADDgVillGxLF4KUUv8BdAJ/MzsWIyilYoD/AH5idiwB5ACGAnOBHwCvKqWUuSH1Tygk8HKgXGud3/35CroSeji4DDikta7WWruBN4ALTI4pEKqUUukA3f+H3KXquSilbgeuAm7T4TOxYhxdHYvt3RuXjwS2KqXSTI3KWOXAG7rLJsBL1wJXIcPyCVxrXQmUKaXGdz+0CNhtYkhGOgzMVUrFdL/zLyJMbtD28A5we/fHtwNvmxiLoZRSVwI/BK7WWreaHY9RtNY7tdYpWuux3RuXlwMzu/8ew8VbwEIApdR5QCShszohEAIJvNsDwN+UUjuA6cD/mhuOMbqvKlYAW4GddP08Qnp6r1LqFWADMF4pVa6Uugv4NXC5UqqIroqGX5sZo6/Ocm6PAnHA+0qpz5VSfzE1SB+d5dzCxlnO71kgs7u08O/A7aF2BSVT6YUQIkSFSg9cCCFED5LAhRAiREkCF0KIECUJXAghQpQkcCGECFGSwIUQIkRJAhdCiBD1/wFWcMydO6LVTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：三角形+虚线\n",
    "plt.plot(\"key1\",\"key2\",\"^--\",data=myData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ed9464-0585-4ad5-91cf-2b4dfe6c57cd",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.4 线颜色设置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0142f086-82af-4c9d-a957-d249d5cad35e",
   "metadata": {
    "tags": []
   },
   "source": [
    "<img src=\"images/线颜色.jpg\" alt=\"线颜色\" style=\"zoom: 55%;\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e950cf3c-bcfa-44a0-a3e8-837b3d606c7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:04:35.587099Z",
     "iopub.status.busy": "2022-06-11T04:04:35.586651Z",
     "iopub.status.idle": "2022-06-11T04:04:35.699328Z",
     "shell.execute_reply": "2022-06-11T04:04:35.698479Z",
     "shell.execute_reply.started": "2022-06-11T04:04:35.587075Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17b2c96f0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：黑色+虚线\n",
    "plt.plot(\"key1\",\"key2\",\"k--\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da4e6d0d-1d7f-4ecd-9fa9-70bffedb38d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:05:10.614710Z",
     "iopub.status.busy": "2022-06-11T04:05:10.614382Z",
     "iopub.status.idle": "2022-06-11T04:05:10.732601Z",
     "shell.execute_reply": "2022-06-11T04:05:10.731902Z",
     "shell.execute_reply.started": "2022-06-11T04:05:10.614685Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17b0dd1e0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：红色\n",
    "plt.plot(\"key1\",\"key2\",\"r\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c84cf3e-0af8-4f8c-8e13-82ab00363c21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:07:18.293425Z",
     "iopub.status.busy": "2022-06-11T04:07:18.293039Z",
     "iopub.status.idle": "2022-06-11T04:07:18.409795Z",
     "shell.execute_reply": "2022-06-11T04:07:18.408938Z",
     "shell.execute_reply.started": "2022-06-11T04:07:18.293400Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17b22b8b0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：红色（用关键字指定颜色）\n",
    "plt.plot(\"key1\",\"key2\",color=\"red\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4f81346-8482-4ea0-bd34-2107274fa89f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:07:49.632472Z",
     "iopub.status.busy": "2022-06-11T04:07:49.632153Z",
     "iopub.status.idle": "2022-06-11T04:07:49.754296Z",
     "shell.execute_reply": "2022-06-11T04:07:49.753286Z",
     "shell.execute_reply.started": "2022-06-11T04:07:49.632449Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112db8e50>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：棕色（用16进制指定）\n",
    "plt.plot(\"key1\",\"key2\",color=\"#893f23\",data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78bb758b-ce27-40bd-9fcf-4af05d1acee4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-11T04:09:18.524882Z",
     "iopub.status.busy": "2022-06-11T04:09:18.524300Z",
     "iopub.status.idle": "2022-06-11T04:09:18.644915Z",
     "shell.execute_reply": "2022-06-11T04:09:18.644075Z",
     "shell.execute_reply.started": "2022-06-11T04:09:18.524857Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17ba030a0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制：（用rgb指定）\n",
    "plt.plot(\"key1\",\"key2\",color=(0.2,0.4,0.4,1),data=myData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fc0afec-eecf-4519-bff8-4e589d66694d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
